Overview

Dataset statistics

Number of variables55
Number of observations65698
Missing cells432747
Missing cells (%)12.0%
Total size in memory27.6 MiB
Average record size in memory440.0 B

Variable types

Text14
Numeric41

Alerts

fga_home has 15447 (23.5%) missing valuesMissing
fg_pct_home has 15490 (23.6%) missing valuesMissing
fg3m_home has 13218 (20.1%) missing valuesMissing
fg3a_home has 18683 (28.4%) missing valuesMissing
fg3_pct_home has 19074 (29.0%) missing valuesMissing
fta_home has 3004 (4.6%) missing valuesMissing
ft_pct_home has 3009 (4.6%) missing valuesMissing
oreb_home has 18936 (28.8%) missing valuesMissing
dreb_home has 18999 (28.9%) missing valuesMissing
reb_home has 15729 (23.9%) missing valuesMissing
ast_home has 15805 (24.1%) missing valuesMissing
stl_home has 18849 (28.7%) missing valuesMissing
blk_home has 18626 (28.4%) missing valuesMissing
tov_home has 18684 (28.4%) missing valuesMissing
pf_home has 2856 (4.3%) missing valuesMissing
fga_away has 15447 (23.5%) missing valuesMissing
fg_pct_away has 15489 (23.6%) missing valuesMissing
fg3m_away has 13218 (20.1%) missing valuesMissing
fg3a_away has 18683 (28.4%) missing valuesMissing
fg3_pct_away has 18962 (28.9%) missing valuesMissing
fta_away has 3004 (4.6%) missing valuesMissing
ft_pct_away has 3006 (4.6%) missing valuesMissing
oreb_away has 18936 (28.8%) missing valuesMissing
dreb_away has 18998 (28.9%) missing valuesMissing
reb_away has 15725 (23.9%) missing valuesMissing
ast_away has 15801 (24.1%) missing valuesMissing
stl_away has 18849 (28.7%) missing valuesMissing
blk_away has 18625 (28.3%) missing valuesMissing
tov_away has 18685 (28.4%) missing valuesMissing
pf_away has 2851 (4.3%) missing valuesMissing
min has 5613 (8.5%) zerosZeros
fg3m_home has 6074 (9.2%) zerosZeros
fg3_pct_home has 2299 (3.5%) zerosZeros
video_available_home has 52471 (79.9%) zerosZeros
fg3m_away has 5773 (8.8%) zerosZeros
fg3_pct_away has 2429 (3.7%) zerosZeros
blk_away has 862 (1.3%) zerosZeros
video_available_away has 52471 (79.9%) zerosZeros

Reproduction

Analysis started2023-07-13 14:04:33.023501
Analysis finished2023-07-13 14:04:34.098956
Duration1.08 second
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

season_id
Text

Distinct225
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:34.349525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters328490
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)< 0.1%

Sample

1st row21946
2nd row21946
3rd row21946
4th row21946
5th row21946
ValueCountFrequency (%)
22016 1230
 
1.9%
22022 1230
 
1.9%
22017 1230
 
1.9%
22009 1230
 
1.9%
22007 1230
 
1.9%
22010 1230
 
1.9%
22006 1230
 
1.9%
22005 1230
 
1.9%
22004 1230
 
1.9%
22014 1230
 
1.9%
Other values (215) 53398
81.3%
2023-07-13T22:04:34.686923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 100383
30.6%
1 56848
17.3%
9 55007
16.7%
0 49542
15.1%
8 17160
 
5.2%
7 13108
 
4.0%
4 11781
 
3.6%
6 9189
 
2.8%
5 9029
 
2.7%
3 6443
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 328490
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 100383
30.6%
1 56848
17.3%
9 55007
16.7%
0 49542
15.1%
8 17160
 
5.2%
7 13108
 
4.0%
4 11781
 
3.6%
6 9189
 
2.8%
5 9029
 
2.7%
3 6443
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 328490
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 100383
30.6%
1 56848
17.3%
9 55007
16.7%
0 49542
15.1%
8 17160
 
5.2%
7 13108
 
4.0%
4 11781
 
3.6%
6 9189
 
2.8%
5 9029
 
2.7%
3 6443
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 328490
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 100383
30.6%
1 56848
17.3%
9 55007
16.7%
0 49542
15.1%
8 17160
 
5.2%
7 13108
 
4.0%
4 11781
 
3.6%
6 9189
 
2.8%
5 9029
 
2.7%
3 6443
 
2.0%

team_id_home
Text

Distinct63
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:34.871920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.997686383
Min length2

Characters and Unicode

Total characters656828
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st row1610610035
2nd row1610610034
3rd row1610610032
4th row1610610025
5th row1610610028
ValueCountFrequency (%)
1610612738 3124
 
4.8%
1610612747 3122
 
4.8%
1610612744 2939
 
4.5%
1610612755 2930
 
4.5%
1610612752 2927
 
4.5%
1610612765 2880
 
4.4%
1610612737 2834
 
4.3%
1610612758 2814
 
4.3%
1610612764 2404
 
3.7%
1610612741 2313
 
3.5%
Other values (53) 37411
56.9%
2023-07-13T22:04:35.109037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 202498
30.8%
6 150635
22.9%
0 72638
 
11.1%
7 72574
 
11.0%
2 71800
 
10.9%
5 30067
 
4.6%
4 28921
 
4.4%
3 13581
 
2.1%
8 7642
 
1.2%
9 6472
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 656828
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202498
30.8%
6 150635
22.9%
0 72638
 
11.1%
7 72574
 
11.0%
2 71800
 
10.9%
5 30067
 
4.6%
4 28921
 
4.4%
3 13581
 
2.1%
8 7642
 
1.2%
9 6472
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 656828
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202498
30.8%
6 150635
22.9%
0 72638
 
11.1%
7 72574
 
11.0%
2 71800
 
10.9%
5 30067
 
4.6%
4 28921
 
4.4%
3 13581
 
2.1%
8 7642
 
1.2%
9 6472
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 656828
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202498
30.8%
6 150635
22.9%
0 72638
 
11.1%
7 72574
 
11.0%
2 71800
 
10.9%
5 30067
 
4.6%
4 28921
 
4.4%
3 13581
 
2.1%
8 7642
 
1.2%
9 6472
 
1.0%

team_abbreviation_home
Text

Distinct97
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:35.283360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.99960425
Min length2

Characters and Unicode

Total characters197068
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowHUS
2nd rowBOM
3rd rowPRO
4th rowCHS
5th rowDEF
ValueCountFrequency (%)
bos 3124
 
4.8%
nyk 2927
 
4.5%
lal 2661
 
4.1%
det 2545
 
3.9%
chi 2313
 
3.5%
mil 2253
 
3.4%
phx 2253
 
3.4%
atl 2217
 
3.4%
hou 2171
 
3.3%
cle 2146
 
3.3%
Other values (87) 41088
62.5%
2023-07-13T22:04:35.512560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 19746
 
10.0%
L 19629
 
10.0%
S 15742
 
8.0%
N 14791
 
7.5%
O 13386
 
6.8%
H 13020
 
6.6%
C 11467
 
5.8%
I 11335
 
5.8%
E 9390
 
4.8%
T 9243
 
4.7%
Other values (15) 59319
30.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 197068
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 19746
 
10.0%
L 19629
 
10.0%
S 15742
 
8.0%
N 14791
 
7.5%
O 13386
 
6.8%
H 13020
 
6.6%
C 11467
 
5.8%
I 11335
 
5.8%
E 9390
 
4.8%
T 9243
 
4.7%
Other values (15) 59319
30.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 197068
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 19746
 
10.0%
L 19629
 
10.0%
S 15742
 
8.0%
N 14791
 
7.5%
O 13386
 
6.8%
H 13020
 
6.6%
C 11467
 
5.8%
I 11335
 
5.8%
E 9390
 
4.8%
T 9243
 
4.7%
Other values (15) 59319
30.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 197068
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 19746
 
10.0%
L 19629
 
10.0%
S 15742
 
8.0%
N 14791
 
7.5%
O 13386
 
6.8%
H 13020
 
6.6%
C 11467
 
5.8%
I 11335
 
5.8%
E 9390
 
4.8%
T 9243
 
4.7%
Other values (15) 59319
30.1%

team_name_home
Text

Distinct98
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:35.821550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length33
Median length30
Mean length16.14833024
Min length9

Characters and Unicode

Total characters1060913
Distinct characters55
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st rowToronto Huskies
2nd rowSt. Louis Bombers
3rd rowProvidence Steamrollers
4th rowChicago Stags
5th rowDetroit Falcons
ValueCountFrequency (%)
new 5391
 
3.6%
los 3931
 
2.6%
angeles 3931
 
2.6%
boston 3124
 
2.1%
celtics 3124
 
2.1%
lakers 3122
 
2.1%
warriors 2939
 
2.0%
york 2927
 
1.9%
knicks 2927
 
1.9%
philadelphia 2927
 
1.9%
Other values (153) 116192
77.2%
2023-07-13T22:04:36.132388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 91518
 
8.6%
s 88506
 
8.3%
a 85878
 
8.1%
84837
 
8.0%
o 65887
 
6.2%
n 64965
 
6.1%
i 64131
 
6.0%
t 60678
 
5.7%
l 59730
 
5.6%
r 58379
 
5.5%
Other values (45) 336404
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 819391
77.2%
Uppercase Letter 150643
 
14.2%
Space Separator 84837
 
8.0%
Decimal Number 4871
 
0.5%
Other Punctuation 971
 
0.1%
Dash Punctuation 200
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 91518
11.2%
s 88506
10.8%
a 85878
10.5%
o 65887
8.0%
n 64965
7.9%
i 64131
7.8%
t 60678
 
7.4%
l 59730
 
7.3%
r 58379
 
7.1%
c 24667
 
3.0%
Other values (15) 155052
18.9%
Uppercase Letter
ValueCountFrequency (%)
S 17036
11.3%
C 15071
 
10.0%
B 13548
 
9.0%
P 12726
 
8.4%
M 9988
 
6.6%
N 9905
 
6.6%
A 8836
 
5.9%
L 7982
 
5.3%
H 7931
 
5.3%
D 6762
 
4.5%
Other values (14) 40858
27.1%
Decimal Number
ValueCountFrequency (%)
7 2436
50.0%
6 2435
50.0%
Other Punctuation
ValueCountFrequency (%)
. 889
91.6%
/ 82
 
8.4%
Space Separator
ValueCountFrequency (%)
84837
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 970034
91.4%
Common 90879
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 91518
 
9.4%
s 88506
 
9.1%
a 85878
 
8.9%
o 65887
 
6.8%
n 64965
 
6.7%
i 64131
 
6.6%
t 60678
 
6.3%
l 59730
 
6.2%
r 58379
 
6.0%
c 24667
 
2.5%
Other values (39) 305695
31.5%
Common
ValueCountFrequency (%)
84837
93.4%
7 2436
 
2.7%
6 2435
 
2.7%
. 889
 
1.0%
- 200
 
0.2%
/ 82
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1060913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 91518
 
8.6%
s 88506
 
8.3%
a 85878
 
8.1%
84837
 
8.0%
o 65887
 
6.2%
n 64965
 
6.1%
i 64131
 
6.0%
t 60678
 
5.7%
l 59730
 
5.6%
r 58379
 
5.5%
Other values (45) 336404
31.7%

game_id
Text

Distinct65642
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:36.375456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters656980
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65586 ?
Unique (%)99.8%

Sample

1st row0024600001
2nd row0024600003
3rd row0024600002
4th row0024600004
5th row0024600005
ValueCountFrequency (%)
0032200001 2
 
< 0.1%
0038600001 2
 
< 0.1%
0038500001 2
 
< 0.1%
0036400001 2
 
< 0.1%
0038700001 2
 
< 0.1%
0038200001 2
 
< 0.1%
0037400101 2
 
< 0.1%
0038300010 2
 
< 0.1%
0035300004 2
 
< 0.1%
0030300001 2
 
< 0.1%
Other values (65632) 65678
> 99.9%
2023-07-13T22:04:36.664557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 303140
46.1%
2 92849
 
14.1%
1 51401
 
7.8%
9 34683
 
5.3%
8 33827
 
5.1%
4 30585
 
4.7%
7 30204
 
4.6%
5 27107
 
4.1%
6 26809
 
4.1%
3 26375
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 656980
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 303140
46.1%
2 92849
 
14.1%
1 51401
 
7.8%
9 34683
 
5.3%
8 33827
 
5.1%
4 30585
 
4.7%
7 30204
 
4.6%
5 27107
 
4.1%
6 26809
 
4.1%
3 26375
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 656980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 303140
46.1%
2 92849
 
14.1%
1 51401
 
7.8%
9 34683
 
5.3%
8 33827
 
5.1%
4 30585
 
4.7%
7 30204
 
4.6%
5 27107
 
4.1%
6 26809
 
4.1%
3 26375
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 656980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 303140
46.1%
2 92849
 
14.1%
1 51401
 
7.8%
9 34683
 
5.3%
8 33827
 
5.1%
4 30585
 
4.7%
7 30204
 
4.6%
5 27107
 
4.1%
6 26809
 
4.1%
3 26375
 
4.0%

game_date
Text

Distinct12882
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:36.922834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1248262
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1950 ?
Unique (%)3.0%

Sample

1st row1946-11-01 00:00:00
2nd row1946-11-02 00:00:00
3rd row1946-11-02 00:00:00
4th row1946-11-02 00:00:00
5th row1946-11-02 00:00:00
ValueCountFrequency (%)
00:00:00 65698
50.0%
2022-11-07 15
 
< 0.1%
2009-01-02 15
 
< 0.1%
2016-11-25 15
 
< 0.1%
2023-04-09 15
 
< 0.1%
2022-04-10 15
 
< 0.1%
2014-04-16 15
 
< 0.1%
2011-04-13 15
 
< 0.1%
2021-05-16 15
 
< 0.1%
2017-04-12 14
 
< 0.1%
Other values (12873) 65564
49.9%
2023-07-13T22:04:37.231913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 515828
41.3%
- 131396
 
10.5%
: 131396
 
10.5%
1 129495
 
10.4%
2 89939
 
7.2%
65698
 
5.3%
9 60472
 
4.8%
3 27309
 
2.2%
8 23492
 
1.9%
4 20786
 
1.7%
Other values (3) 52451
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 919772
73.7%
Dash Punctuation 131396
 
10.5%
Other Punctuation 131396
 
10.5%
Space Separator 65698
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 515828
56.1%
1 129495
 
14.1%
2 89939
 
9.8%
9 60472
 
6.6%
3 27309
 
3.0%
8 23492
 
2.6%
4 20786
 
2.3%
7 18676
 
2.0%
5 17903
 
1.9%
6 15872
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 131396
100.0%
Other Punctuation
ValueCountFrequency (%)
: 131396
100.0%
Space Separator
ValueCountFrequency (%)
65698
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1248262
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 515828
41.3%
- 131396
 
10.5%
: 131396
 
10.5%
1 129495
 
10.4%
2 89939
 
7.2%
65698
 
5.3%
9 60472
 
4.8%
3 27309
 
2.2%
8 23492
 
1.9%
4 20786
 
1.7%
Other values (3) 52451
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1248262
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 515828
41.3%
- 131396
 
10.5%
: 131396
 
10.5%
1 129495
 
10.4%
2 89939
 
7.2%
65698
 
5.3%
9 60472
 
4.8%
3 27309
 
2.2%
8 23492
 
1.9%
4 20786
 
1.7%
Other values (3) 52451
 
4.2%

matchup_home
Text

Distinct2292
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:37.384838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.99905629
Min length10

Characters and Unicode

Total characters722616
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique176 ?
Unique (%)0.3%

Sample

1st rowHUS vs. NYK
2nd rowBOM vs. PIT
3rd rowPRO vs. BOS
4th rowCHS vs. NYK
5th rowDEF vs. WAS
ValueCountFrequency (%)
vs 65698
33.3%
bos 6207
 
3.1%
nyk 5877
 
3.0%
lal 5203
 
2.6%
det 5110
 
2.6%
chi 4608
 
2.3%
mil 4510
 
2.3%
phx 4490
 
2.3%
atl 4450
 
2.3%
hou 4333
 
2.2%
Other values (107) 86608
43.9%
2023-07-13T22:04:37.582740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
131396
18.2%
v 65698
 
9.1%
s 65698
 
9.1%
. 65698
 
9.1%
A 39486
 
5.5%
L 39033
 
5.4%
S 31435
 
4.4%
N 29700
 
4.1%
O 26696
 
3.7%
H 26122
 
3.6%
Other values (19) 201654
27.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 394126
54.5%
Space Separator 131396
 
18.2%
Lowercase Letter 131396
 
18.2%
Other Punctuation 65698
 
9.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 39486
 
10.0%
L 39033
 
9.9%
S 31435
 
8.0%
N 29700
 
7.5%
O 26696
 
6.8%
H 26122
 
6.6%
C 22875
 
5.8%
I 22703
 
5.8%
E 18789
 
4.8%
T 18493
 
4.7%
Other values (15) 118794
30.1%
Lowercase Letter
ValueCountFrequency (%)
v 65698
50.0%
s 65698
50.0%
Space Separator
ValueCountFrequency (%)
131396
100.0%
Other Punctuation
ValueCountFrequency (%)
. 65698
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 525522
72.7%
Common 197094
 
27.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
v 65698
12.5%
s 65698
12.5%
A 39486
 
7.5%
L 39033
 
7.4%
S 31435
 
6.0%
N 29700
 
5.7%
O 26696
 
5.1%
H 26122
 
5.0%
C 22875
 
4.4%
I 22703
 
4.3%
Other values (17) 156076
29.7%
Common
ValueCountFrequency (%)
131396
66.7%
. 65698
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 722616
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
131396
18.2%
v 65698
 
9.1%
s 65698
 
9.1%
. 65698
 
9.1%
A 39486
 
5.5%
L 39033
 
5.4%
S 31435
 
4.4%
N 29700
 
4.1%
O 26696
 
3.7%
H 26122
 
3.6%
Other values (19) 201654
27.9%

wl_home
Text

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size513.4 KiB
2023-07-13T22:04:37.635926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters65696
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowL
2nd rowW
3rd rowW
4th rowW
5th rowL
ValueCountFrequency (%)
w 40649
61.9%
l 25047
38.1%
2023-07-13T22:04:37.728798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
W 40649
61.9%
L 25047
38.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 65696
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
W 40649
61.9%
L 25047
38.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 65696
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 40649
61.9%
L 25047
38.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 40649
61.9%
L 25047
38.1%

min
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean221.0034856
Minimum0
Maximum365
Zeros5613
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:37.783649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1240
median240
Q3240
95-th percentile265
Maximum365
Range365
Interquartile range (IQR)0

Descriptive statistics

Standard deviation67.90352105
Coefficient of variation (CV)0.3072509054
Kurtosis6.612541692
Mean221.0034856
Median Absolute Deviation (MAD)0
Skewness-2.904808235
Sum14519487
Variance4610.888171
MonotonicityNot monotonic
2023-07-13T22:04:37.827674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
240 56666
86.3%
0 5613
 
8.5%
265 2937
 
4.5%
290 408
 
0.6%
315 57
 
0.1%
340 11
 
< 0.1%
221 2
 
< 0.1%
365 1
 
< 0.1%
120 1
 
< 0.1%
220 1
 
< 0.1%
ValueCountFrequency (%)
0 5613
8.5%
120 1
 
< 0.1%
180 1
 
< 0.1%
220 1
 
< 0.1%
221 2
 
< 0.1%
ValueCountFrequency (%)
365 1
 
< 0.1%
340 11
 
< 0.1%
315 57
 
0.1%
290 408
 
0.6%
265 2937
4.5%

fgm_home
Real number (ℝ)

Distinct64
Distinct (%)0.1%
Missing13
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean39.67226916
Minimum4
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:37.885466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile29
Q135
median40
Q344
95-th percentile51
Maximum84
Range80
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.770802477
Coefficient of variation (CV)0.1706683943
Kurtosis0.4318556557
Mean39.67226916
Median Absolute Deviation (MAD)4
Skewness0.1128140957
Sum2605873
Variance45.84376619
MonotonicityNot monotonic
2023-07-13T22:04:37.945373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 4002
 
6.1%
40 3899
 
5.9%
38 3891
 
5.9%
41 3888
 
5.9%
37 3729
 
5.7%
42 3716
 
5.7%
36 3541
 
5.4%
43 3465
 
5.3%
35 3152
 
4.8%
44 3058
 
4.7%
Other values (54) 29344
44.7%
ValueCountFrequency (%)
4 2
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
12 2
< 0.1%
14 2
< 0.1%
ValueCountFrequency (%)
84 2
 
< 0.1%
79 2
 
< 0.1%
75 2
 
< 0.1%
72 1
 
< 0.1%
69 7
< 0.1%

fga_home
Real number (ℝ)

MISSING 

Distinct87
Distinct (%)0.2%
Missing15447
Missing (%)23.5%
Infinite0
Infinite (%)0.0%
Mean83.99279616
Minimum0
Maximum240
Zeros43
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:38.007703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile70
Q178
median84
Q389
95-th percentile99
Maximum240
Range240
Interquartile range (IQR)11

Descriptive statistics

Standard deviation9.164445023
Coefficient of variation (CV)0.1091098932
Kurtosis8.306595499
Mean83.99279616
Median Absolute Deviation (MAD)6
Skewness-0.153595825
Sum4220722
Variance83.98705258
MonotonicityNot monotonic
2023-07-13T22:04:38.069081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83 2397
 
3.6%
82 2369
 
3.6%
84 2339
 
3.6%
85 2335
 
3.6%
81 2325
 
3.5%
80 2217
 
3.4%
86 2205
 
3.4%
79 2148
 
3.3%
87 2121
 
3.2%
78 2012
 
3.1%
Other values (77) 27783
42.3%
(Missing) 15447
23.5%
ValueCountFrequency (%)
0 43
0.1%
29 1
 
< 0.1%
53 2
 
< 0.1%
54 2
 
< 0.1%
55 4
 
< 0.1%
ValueCountFrequency (%)
240 1
< 0.1%
183 1
< 0.1%
143 2
< 0.1%
140 2
< 0.1%
137 2
< 0.1%

fg_pct_home
Real number (ℝ)

MISSING 

Distinct415
Distinct (%)0.8%
Missing15490
Missing (%)23.6%
Infinite0
Infinite (%)0.0%
Mean0.4673209847
Minimum0.14
Maximum0.697
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:38.128860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.14
5-th percentile0.372
Q10.427
median0.467
Q30.506
95-th percentile0.566
Maximum0.697
Range0.557
Interquartile range (IQR)0.079

Descriptive statistics

Standard deviation0.05942343713
Coefficient of variation (CV)0.1271576477
Kurtosis0.09618242617
Mean0.4673209847
Median Absolute Deviation (MAD)0.04
Skewness0.04023830252
Sum23463.252
Variance0.00353114488
MonotonicityNot monotonic
2023-07-13T22:04:38.185050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 1679
 
2.6%
0.494 1055
 
1.6%
0.506 1038
 
1.6%
0.488 688
 
1.0%
0.481 545
 
0.8%
0.471 493
 
0.8%
0.4 486
 
0.7%
0.429 483
 
0.7%
0.43 455
 
0.7%
0.463 454
 
0.7%
Other values (405) 42832
65.2%
(Missing) 15490
 
23.6%
ValueCountFrequency (%)
0.14 1
< 0.1%
0.169 1
< 0.1%
0.172 1
< 0.1%
0.173 1
< 0.1%
0.181 1
< 0.1%
ValueCountFrequency (%)
0.697 1
< 0.1%
0.693 1
< 0.1%
0.691 1
< 0.1%
0.687 1
< 0.1%
0.686 1
< 0.1%

fg3m_home
Real number (ℝ)

MISSING  ZEROS 

Distinct29
Distinct (%)0.1%
Missing13218
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean5.735099085
Minimum0
Maximum28
Zeros6074
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:38.238690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile14
Maximum28
Range28
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.537336503
Coefficient of variation (CV)0.7911522426
Kurtosis0.3823028021
Mean5.735099085
Median Absolute Deviation (MAD)3
Skewness0.8256194837
Sum300978
Variance20.58742254
MonotonicityNot monotonic
2023-07-13T22:04:38.357018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 6074
9.2%
1 4723
 
7.2%
4 4455
 
6.8%
3 4410
 
6.7%
5 4404
 
6.7%
2 4281
 
6.5%
6 4130
 
6.3%
7 3717
 
5.7%
8 3103
 
4.7%
9 2749
 
4.2%
Other values (19) 10434
15.9%
(Missing) 13218
20.1%
ValueCountFrequency (%)
0 6074
9.2%
1 4723
7.2%
2 4281
6.5%
3 4410
6.7%
4 4455
6.8%
ValueCountFrequency (%)
28 1
 
< 0.1%
27 8
< 0.1%
26 8
< 0.1%
25 14
< 0.1%
24 11
< 0.1%

fg3a_home
Real number (ℝ)

MISSING 

Distinct69
Distinct (%)0.1%
Missing18683
Missing (%)28.4%
Infinite0
Infinite (%)0.0%
Mean17.74114644
Minimum0
Maximum77
Zeros391
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:38.418998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q110
median16
Q324
95-th percentile37
Maximum77
Range77
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.54580965
Coefficient of variation (CV)0.5944266163
Kurtosis0.1040025152
Mean17.74114644
Median Absolute Deviation (MAD)7
Skewness0.6505988277
Sum834100
Variance111.2141012
MonotonicityNot monotonic
2023-07-13T22:04:38.479772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 1914
 
2.9%
15 1914
 
2.9%
13 1871
 
2.8%
16 1846
 
2.8%
11 1812
 
2.8%
12 1770
 
2.7%
17 1733
 
2.6%
10 1677
 
2.6%
18 1642
 
2.5%
9 1619
 
2.5%
Other values (59) 29217
44.5%
(Missing) 18683
28.4%
ValueCountFrequency (%)
0 391
 
0.6%
1 671
1.0%
2 912
1.4%
3 1041
1.6%
4 1136
1.7%
ValueCountFrequency (%)
77 2
< 0.1%
72 2
< 0.1%
70 1
 
< 0.1%
68 3
< 0.1%
65 2
< 0.1%

fg3_pct_home
Real number (ℝ)

MISSING  ZEROS 

Distinct395
Distinct (%)0.8%
Missing19074
Missing (%)29.0%
Infinite0
Infinite (%)0.0%
Mean0.34613596
Minimum0
Maximum1
Zeros2299
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:38.543813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.063
Q10.261
median0.348
Q30.42975
95-th percentile0.571
Maximum1
Range1
Interquartile range (IQR)0.16875

Descriptive statistics

Standard deviation0.1512336488
Coefficient of variation (CV)0.4369197837
Kurtosis1.860790395
Mean0.34613596
Median Absolute Deviation (MAD)0.085
Skewness0.2229517037
Sum16138.243
Variance0.02287161652
MonotonicityNot monotonic
2023-07-13T22:04:38.604781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.333 3404
 
5.2%
0.5 2662
 
4.1%
0 2299
 
3.5%
0.25 1891
 
2.9%
0.4 1792
 
2.7%
0.375 1126
 
1.7%
0.286 1108
 
1.7%
0.2 1084
 
1.6%
0.429 1037
 
1.6%
0.364 769
 
1.2%
Other values (385) 29452
44.8%
(Missing) 19074
29.0%
ValueCountFrequency (%)
0 2299
3.5%
0.045 1
 
< 0.1%
0.048 2
 
< 0.1%
0.05 2
 
< 0.1%
0.053 4
 
< 0.1%
ValueCountFrequency (%)
1 273
0.4%
0.889 1
 
< 0.1%
0.875 3
 
< 0.1%
0.857 11
 
< 0.1%
0.842 1
 
< 0.1%

ftm_home
Real number (ℝ)

Distinct61
Distinct (%)0.1%
Missing16
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean20.69302092
Minimum0
Maximum61
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:38.664398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q116
median20
Q325
95-th percentile33
Maximum61
Range61
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.138850853
Coefficient of variation (CV)0.344988336
Kurtosis0.1881928521
Mean20.69302092
Median Absolute Deviation (MAD)5
Skewness0.4313798561
Sum1359159
Variance50.9631915
MonotonicityNot monotonic
2023-07-13T22:04:38.723129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 3728
 
5.7%
17 3591
 
5.5%
18 3585
 
5.5%
19 3582
 
5.5%
21 3553
 
5.4%
22 3513
 
5.3%
16 3298
 
5.0%
15 3219
 
4.9%
23 3095
 
4.7%
24 3004
 
4.6%
Other values (51) 31514
48.0%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 2
 
< 0.1%
2 14
 
< 0.1%
3 39
0.1%
4 72
0.1%
ValueCountFrequency (%)
61 1
< 0.1%
60 1
< 0.1%
59 1
< 0.1%
57 2
< 0.1%
56 1
< 0.1%

fta_home
Real number (ℝ)

MISSING 

Distinct76
Distinct (%)0.1%
Missing3004
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean27.1415925
Minimum0
Maximum86
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:38.788927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q121
median27
Q333
95-th percentile43
Maximum86
Range86
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.848790176
Coefficient of variation (CV)0.3260232493
Kurtosis0.2027405429
Mean27.1415925
Median Absolute Deviation (MAD)6
Skewness0.4131331181
Sum1701615
Variance78.30108758
MonotonicityNot monotonic
2023-07-13T22:04:38.849727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 2821
 
4.3%
25 2809
 
4.3%
24 2788
 
4.2%
27 2783
 
4.2%
28 2762
 
4.2%
23 2717
 
4.1%
22 2663
 
4.1%
29 2651
 
4.0%
21 2590
 
3.9%
30 2489
 
3.8%
Other values (66) 35621
54.2%
(Missing) 3004
 
4.6%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 1
 
< 0.1%
2 3
 
< 0.1%
3 4
 
< 0.1%
4 12
< 0.1%
ValueCountFrequency (%)
86 1
< 0.1%
82 1
< 0.1%
80 1
< 0.1%
74 1
< 0.1%
71 1
< 0.1%

ft_pct_home
Real number (ℝ)

MISSING 

Distinct442
Distinct (%)0.7%
Missing3009
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean0.7554175852
Minimum0
Maximum4.167
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:38.910777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.588
Q10.694
median0.76
Q30.821
95-th percentile0.905
Maximum4.167
Range4.167
Interquartile range (IQR)0.127

Descriptive statistics

Standard deviation0.09861157553
Coefficient of variation (CV)0.130539158
Kurtosis25.42788205
Mean0.7554175852
Median Absolute Deviation (MAD)0.064
Skewness0.4657797193
Sum47356.373
Variance0.009724242829
MonotonicityNot monotonic
2023-07-13T22:04:38.967966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.75 2590
 
3.9%
0.8 2047
 
3.1%
0.667 2017
 
3.1%
0.833 1472
 
2.2%
0.714 1300
 
2.0%
0.778 1114
 
1.7%
0.857 982
 
1.5%
0.727 856
 
1.3%
0.818 853
 
1.3%
0.769 792
 
1.2%
Other values (432) 48666
74.1%
(Missing) 3009
 
4.6%
ValueCountFrequency (%)
0 1
< 0.1%
0.143 1
< 0.1%
0.167 1
< 0.1%
0.211 1
< 0.1%
0.214 1
< 0.1%
ValueCountFrequency (%)
4.167 1
< 0.1%
2.6 1
< 0.1%
1.778 1
< 0.1%
1.714 1
< 0.1%
1.419 1
< 0.1%

oreb_home
Real number (ℝ)

MISSING 

Distinct37
Distinct (%)0.1%
Missing18936
Missing (%)28.8%
Infinite0
Infinite (%)0.0%
Mean12.11068817
Minimum0
Maximum44
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:39.023476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q19
median12
Q315
95-th percentile20
Maximum44
Range44
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.437155967
Coefficient of variation (CV)0.366383471
Kurtosis0.5712128814
Mean12.11068817
Median Absolute Deviation (MAD)3
Skewness0.5607098369
Sum566320
Variance19.68835307
MonotonicityNot monotonic
2023-07-13T22:04:39.074810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
11 4432
 
6.7%
12 4272
 
6.5%
10 4261
 
6.5%
13 3892
 
5.9%
9 3862
 
5.9%
14 3483
 
5.3%
8 3258
 
5.0%
15 2923
 
4.4%
7 2610
 
4.0%
16 2371
 
3.6%
Other values (27) 11398
17.3%
(Missing) 18936
28.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 28
 
< 0.1%
2 104
 
0.2%
3 306
0.5%
4 663
1.0%
ValueCountFrequency (%)
44 1
 
< 0.1%
37 2
 
< 0.1%
34 1
 
< 0.1%
33 7
< 0.1%
32 10
< 0.1%

dreb_home
Real number (ℝ)

MISSING 

Distinct48
Distinct (%)0.1%
Missing18999
Missing (%)28.9%
Infinite0
Infinite (%)0.0%
Mean31.41152915
Minimum3
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:39.128407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile22
Q128
median31
Q335
95-th percentile41
Maximum56
Range53
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.612730708
Coefficient of variation (CV)0.1786837782
Kurtosis0.04328271988
Mean31.41152915
Median Absolute Deviation (MAD)4
Skewness0.1602329869
Sum1466887
Variance31.502746
MonotonicityNot monotonic
2023-07-13T22:04:39.186862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
30 3327
 
5.1%
31 3308
 
5.0%
32 3306
 
5.0%
29 3163
 
4.8%
33 3063
 
4.7%
34 2989
 
4.5%
28 2811
 
4.3%
27 2593
 
3.9%
35 2510
 
3.8%
36 2244
 
3.4%
Other values (38) 17385
26.5%
(Missing) 18999
28.9%
ValueCountFrequency (%)
3 1
 
< 0.1%
10 1
 
< 0.1%
11 2
 
< 0.1%
12 1
 
< 0.1%
13 7
< 0.1%
ValueCountFrequency (%)
56 1
 
< 0.1%
55 3
 
< 0.1%
54 2
 
< 0.1%
53 4
< 0.1%
52 9
< 0.1%

reb_home
Real number (ℝ)

MISSING 

Distinct68
Distinct (%)0.1%
Missing15729
Missing (%)23.9%
Infinite0
Infinite (%)0.0%
Mean43.76143209
Minimum0
Maximum85
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:39.246020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33
Q139
median43
Q348
95-th percentile56
Maximum85
Range85
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.087954124
Coefficient of variation (CV)0.1619680569
Kurtosis0.8117802285
Mean43.76143209
Median Absolute Deviation (MAD)5
Skewness0.3049071486
Sum2186715
Variance50.23909366
MonotonicityNot monotonic
2023-07-13T22:04:39.303510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 2968
 
4.5%
44 2860
 
4.4%
41 2848
 
4.3%
43 2830
 
4.3%
45 2814
 
4.3%
40 2618
 
4.0%
46 2605
 
4.0%
47 2494
 
3.8%
39 2360
 
3.6%
38 2157
 
3.3%
Other values (58) 23415
35.6%
(Missing) 15729
23.9%
ValueCountFrequency (%)
0 10
< 0.1%
15 1
 
< 0.1%
17 1
 
< 0.1%
18 3
 
< 0.1%
19 2
 
< 0.1%
ValueCountFrequency (%)
85 3
< 0.1%
83 2
< 0.1%
81 2
< 0.1%
80 3
< 0.1%
79 4
< 0.1%

ast_home
Real number (ℝ)

MISSING 

Distinct50
Distinct (%)0.1%
Missing15805
Missing (%)24.1%
Infinite0
Infinite (%)0.0%
Mean23.94099373
Minimum0
Maximum60
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:39.361609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q120
median24
Q328
95-th percentile34
Maximum60
Range60
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.69405284
Coefficient of variation (CV)0.2378369463
Kurtosis0.331983733
Mean23.94099373
Median Absolute Deviation (MAD)4
Skewness0.3087280729
Sum1194488
Variance32.42223774
MonotonicityNot monotonic
2023-07-13T22:04:39.420222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 3580
 
5.4%
22 3548
 
5.4%
24 3486
 
5.3%
21 3356
 
5.1%
25 3314
 
5.0%
26 3120
 
4.7%
20 2997
 
4.6%
27 2798
 
4.3%
19 2641
 
4.0%
28 2528
 
3.8%
Other values (40) 18525
28.2%
(Missing) 15805
24.1%
ValueCountFrequency (%)
0 11
< 0.1%
4 1
 
< 0.1%
5 5
< 0.1%
6 6
< 0.1%
7 12
< 0.1%
ValueCountFrequency (%)
60 2
 
< 0.1%
52 3
< 0.1%
51 2
 
< 0.1%
50 5
< 0.1%
49 5
< 0.1%

stl_home
Real number (ℝ)

MISSING 

Distinct27
Distinct (%)0.1%
Missing18849
Missing (%)28.7%
Infinite0
Infinite (%)0.0%
Mean7.991782108
Minimum0
Maximum27
Zeros26
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:39.475683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q16
median8
Q310
95-th percentile13
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.110086807
Coefficient of variation (CV)0.3891606108
Kurtosis0.4198114412
Mean7.991782108
Median Absolute Deviation (MAD)2
Skewness0.4934933319
Sum374407
Variance9.672639944
MonotonicityNot monotonic
2023-07-13T22:04:39.609171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
8 6104
 
9.3%
7 6074
 
9.2%
6 5533
 
8.4%
9 5317
 
8.1%
5 4461
 
6.8%
10 4321
 
6.6%
11 3231
 
4.9%
4 3032
 
4.6%
12 2294
 
3.5%
3 1716
 
2.6%
Other values (17) 4766
 
7.3%
(Missing) 18849
28.7%
ValueCountFrequency (%)
0 26
 
< 0.1%
1 214
 
0.3%
2 746
 
1.1%
3 1716
2.6%
4 3032
4.6%
ValueCountFrequency (%)
27 1
 
< 0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 19
< 0.1%

blk_home
Real number (ℝ)

MISSING 

Distinct24
Distinct (%)0.1%
Missing18626
Missing (%)28.4%
Infinite0
Infinite (%)0.0%
Mean5.313689667
Minimum0
Maximum23
Zeros606
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:39.658619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q37
95-th percentile10
Maximum23
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.768968193
Coefficient of variation (CV)0.5211008484
Kurtosis0.8020422722
Mean5.313689667
Median Absolute Deviation (MAD)2
Skewness0.7146930879
Sum250126
Variance7.667184856
MonotonicityNot monotonic
2023-07-13T22:04:39.707980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4 7074
 
10.8%
5 6988
 
10.6%
3 6136
 
9.3%
6 5904
 
9.0%
7 4774
 
7.3%
2 4172
 
6.4%
8 3388
 
5.2%
9 2372
 
3.6%
1 2060
 
3.1%
10 1520
 
2.3%
Other values (14) 2684
 
4.1%
(Missing) 18626
28.4%
ValueCountFrequency (%)
0 606
 
0.9%
1 2060
 
3.1%
2 4172
6.4%
3 6136
9.3%
4 7074
10.8%
ValueCountFrequency (%)
23 1
 
< 0.1%
22 1
 
< 0.1%
21 1
 
< 0.1%
20 5
< 0.1%
19 9
< 0.1%

tov_home
Real number (ℝ)

MISSING 

Distinct37
Distinct (%)0.1%
Missing18684
Missing (%)28.4%
Infinite0
Infinite (%)0.0%
Mean14.78272429
Minimum0
Maximum39
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:39.761442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q112
median15
Q317
95-th percentile22
Maximum39
Range39
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.140003889
Coefficient of variation (CV)0.2800568966
Kurtosis0.2259555788
Mean14.78272429
Median Absolute Deviation (MAD)3
Skewness0.3481250257
Sum694995
Variance17.1396322
MonotonicityNot monotonic
2023-07-13T22:04:39.816952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
14 4611
 
7.0%
13 4497
 
6.8%
15 4435
 
6.8%
16 4210
 
6.4%
12 3945
 
6.0%
17 3650
 
5.6%
11 3432
 
5.2%
18 2986
 
4.5%
10 2625
 
4.0%
19 2351
 
3.6%
Other values (27) 10272
15.6%
(Missing) 18684
28.4%
ValueCountFrequency (%)
0 6
 
< 0.1%
1 1
 
< 0.1%
2 3
 
< 0.1%
3 17
 
< 0.1%
4 73
0.1%
ValueCountFrequency (%)
39 1
 
< 0.1%
36 1
 
< 0.1%
34 2
 
< 0.1%
33 2
 
< 0.1%
32 10
< 0.1%

pf_home
Real number (ℝ)

MISSING 

Distinct55
Distinct (%)0.1%
Missing2856
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean22.38275357
Minimum0
Maximum122
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:39.876303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q119
median22
Q326
95-th percentile31
Maximum122
Range122
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.042668355
Coefficient of variation (CV)0.2252925824
Kurtosis7.49181296
Mean22.38275357
Median Absolute Deviation (MAD)3
Skewness0.741781821
Sum1406577
Variance25.42850413
MonotonicityNot monotonic
2023-07-13T22:04:39.940105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 5118
 
7.8%
21 4987
 
7.6%
23 4870
 
7.4%
20 4841
 
7.4%
24 4620
 
7.0%
19 4442
 
6.8%
25 4078
 
6.2%
18 3882
 
5.9%
26 3558
 
5.4%
17 3100
 
4.7%
Other values (45) 19346
29.4%
ValueCountFrequency (%)
0 3
< 0.1%
2 4
< 0.1%
3 1
 
< 0.1%
5 4
< 0.1%
6 6
< 0.1%
ValueCountFrequency (%)
122 1
< 0.1%
115 1
< 0.1%
107 1
< 0.1%
106 1
< 0.1%
88 1
< 0.1%

pts_home
Real number (ℝ)

Distinct131
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.619136
Minimum18
Maximum192
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:40.003202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile80
Q195
median105
Q3114
95-th percentile129
Maximum192
Range174
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.75792355
Coefficient of variation (CV)0.1410633285
Kurtosis0.3530740512
Mean104.619136
Median Absolute Deviation (MAD)10
Skewness0.002331390626
Sum6873268
Variance217.7963076
MonotonicityNot monotonic
2023-07-13T22:04:40.063394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102 1838
 
2.8%
106 1824
 
2.8%
103 1823
 
2.8%
105 1822
 
2.8%
104 1811
 
2.8%
107 1791
 
2.7%
108 1789
 
2.7%
100 1754
 
2.7%
99 1749
 
2.7%
109 1744
 
2.7%
Other values (121) 47753
72.7%
ValueCountFrequency (%)
18 1
< 0.1%
33 1
< 0.1%
36 1
< 0.1%
44 1
< 0.1%
45 1
< 0.1%
ValueCountFrequency (%)
192 2
< 0.1%
184 1
 
< 0.1%
175 3
< 0.1%
173 4
< 0.1%
171 1
 
< 0.1%

plus_minus_home
Real number (ℝ)

Distinct121
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.627568571
Minimum-68
Maximum73
Zeros0
Zeros (%)0.0%
Negative25046
Negative (%)38.1%
Memory size513.4 KiB
2023-07-13T22:04:40.125961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-68
5-th percentile-18
Q1-5
median4
Q312
95-th percentile25
Maximum73
Range141
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.09139486
Coefficient of variation (CV)3.608862137
Kurtosis0.3444088881
Mean3.627568571
Median Absolute Deviation (MAD)9
Skewness-0.01722224077
Sum238324
Variance171.3846195
MonotonicityNot monotonic
2023-07-13T22:04:40.184369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 2409
 
3.7%
5 2396
 
3.6%
6 2252
 
3.4%
8 2248
 
3.4%
4 2212
 
3.4%
2 2202
 
3.4%
9 2170
 
3.3%
3 2164
 
3.3%
10 1977
 
3.0%
-2 1915
 
2.9%
Other values (111) 43753
66.6%
ValueCountFrequency (%)
-68 1
 
< 0.1%
-58 1
 
< 0.1%
-57 1
 
< 0.1%
-56 4
< 0.1%
-54 1
 
< 0.1%
ValueCountFrequency (%)
73 1
< 0.1%
68 1
< 0.1%
65 1
< 0.1%
63 1
< 0.1%
62 2
< 0.1%

video_available_home
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2013303297
Minimum0
Maximum1
Zeros52471
Zeros (%)79.9%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:40.236604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4009973511
Coefficient of variation (CV)1.991738412
Kurtosis0.2191516246
Mean0.2013303297
Median Absolute Deviation (MAD)0
Skewness1.48967948
Sum13227
Variance0.1607988756
MonotonicityNot monotonic
2023-07-13T22:04:40.278528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 52471
79.9%
1 13227
 
20.1%
ValueCountFrequency (%)
0 52471
79.9%
1 13227
 
20.1%
ValueCountFrequency (%)
1 13227
 
20.1%
0 52471
79.9%

team_id_away
Text

Distinct72
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:40.445691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.993911535
Min length2

Characters and Unicode

Total characters656580
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st row1610612752
2nd row1610610031
3rd row1610612738
4th row1610612752
5th row1610610036
ValueCountFrequency (%)
1610612738 3083
 
4.7%
1610612747 3014
 
4.6%
1610612744 2976
 
4.5%
1610612752 2950
 
4.5%
1610612765 2895
 
4.4%
1610612755 2888
 
4.4%
1610612737 2879
 
4.4%
1610612758 2797
 
4.3%
1610612764 2429
 
3.7%
1610612741 2295
 
3.5%
Other values (62) 37492
57.1%
2023-07-13T22:04:40.684842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 202378
30.8%
6 150606
22.9%
0 72672
 
11.1%
7 72463
 
11.0%
2 71861
 
10.9%
5 30013
 
4.6%
4 28963
 
4.4%
3 13584
 
2.1%
8 7575
 
1.2%
9 6465
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 656580
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 202378
30.8%
6 150606
22.9%
0 72672
 
11.1%
7 72463
 
11.0%
2 71861
 
10.9%
5 30013
 
4.6%
4 28963
 
4.4%
3 13584
 
2.1%
8 7575
 
1.2%
9 6465
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 656580
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 202378
30.8%
6 150606
22.9%
0 72672
 
11.1%
7 72463
 
11.0%
2 71861
 
10.9%
5 30013
 
4.6%
4 28963
 
4.4%
3 13584
 
2.1%
8 7575
 
1.2%
9 6465
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 656580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 202378
30.8%
6 150606
22.9%
0 72672
 
11.1%
7 72463
 
11.0%
2 71861
 
10.9%
5 30013
 
4.6%
4 28963
 
4.4%
3 13584
 
2.1%
8 7575
 
1.2%
9 6465
 
1.0%

team_abbreviation_away
Text

Distinct101
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:40.862484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.999452038
Min length2

Characters and Unicode

Total characters197058
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowNYK
2nd rowPIT
3rd rowBOS
4th rowNYK
5th rowWAS
ValueCountFrequency (%)
bos 3083
 
4.7%
nyk 2950
 
4.5%
det 2565
 
3.9%
lal 2542
 
3.9%
chi 2295
 
3.5%
mil 2257
 
3.4%
phx 2237
 
3.4%
atl 2233
 
3.4%
hou 2162
 
3.3%
por 2152
 
3.3%
Other values (91) 41222
62.7%
2023-07-13T22:04:41.086380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 19740
 
10.0%
L 19404
 
9.8%
S 15693
 
8.0%
N 14909
 
7.6%
O 13310
 
6.8%
H 13102
 
6.6%
C 11408
 
5.8%
I 11368
 
5.8%
E 9399
 
4.8%
T 9250
 
4.7%
Other values (15) 59475
30.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 197058
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 19740
 
10.0%
L 19404
 
9.8%
S 15693
 
8.0%
N 14909
 
7.6%
O 13310
 
6.8%
H 13102
 
6.6%
C 11408
 
5.8%
I 11368
 
5.8%
E 9399
 
4.8%
T 9250
 
4.7%
Other values (15) 59475
30.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 197058
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 19740
 
10.0%
L 19404
 
9.8%
S 15693
 
8.0%
N 14909
 
7.6%
O 13310
 
6.8%
H 13102
 
6.6%
C 11408
 
5.8%
I 11368
 
5.8%
E 9399
 
4.8%
T 9250
 
4.7%
Other values (15) 59475
30.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 197058
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 19740
 
10.0%
L 19404
 
9.8%
S 15693
 
8.0%
N 14909
 
7.6%
O 13310
 
6.8%
H 13102
 
6.6%
C 11408
 
5.8%
I 11368
 
5.8%
E 9399
 
4.8%
T 9250
 
4.7%
Other values (15) 59475
30.2%

team_name_away
Text

Distinct101
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:41.330982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length33
Median length25
Mean length16.14673202
Min length9

Characters and Unicode

Total characters1060808
Distinct characters57
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowNew York Knicks
2nd rowPittsburgh Ironmen
3rd rowBoston Celtics
4th rowNew York Knicks
5th rowWashington Capitols
ValueCountFrequency (%)
new 5437
 
3.6%
los 3811
 
2.5%
angeles 3811
 
2.5%
boston 3083
 
2.0%
celtics 3083
 
2.0%
lakers 3014
 
2.0%
warriors 2976
 
2.0%
philadelphia 2968
 
2.0%
york 2950
 
2.0%
knicks 2950
 
2.0%
Other values (163) 116383
77.3%
2023-07-13T22:04:41.634543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 91435
 
8.6%
s 88186
 
8.3%
a 86029
 
8.1%
84768
 
8.0%
o 65644
 
6.2%
n 64951
 
6.1%
i 64329
 
6.1%
t 60708
 
5.7%
l 59578
 
5.6%
r 58489
 
5.5%
Other values (47) 336691
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 819428
77.2%
Uppercase Letter 150554
 
14.2%
Space Separator 84768
 
8.0%
Decimal Number 4906
 
0.5%
Other Punctuation 963
 
0.1%
Dash Punctuation 189
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 91435
11.2%
s 88186
10.8%
a 86029
10.5%
o 65644
8.0%
n 64951
7.9%
i 64329
7.9%
t 60708
 
7.4%
l 59578
 
7.3%
r 58489
 
7.1%
c 24647
 
3.0%
Other values (15) 155432
19.0%
Uppercase Letter
ValueCountFrequency (%)
S 17011
11.3%
C 14951
 
9.9%
B 13511
 
9.0%
P 12809
 
8.5%
M 10072
 
6.7%
N 9938
 
6.6%
A 8725
 
5.8%
H 8009
 
5.3%
L 7735
 
5.1%
D 6826
 
4.5%
Other values (14) 40967
27.2%
Decimal Number
ValueCountFrequency (%)
6 2453
50.0%
7 2451
50.0%
3 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 873
90.7%
/ 84
 
8.7%
' 6
 
0.6%
Space Separator
ValueCountFrequency (%)
84768
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 969982
91.4%
Common 90826
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 91435
 
9.4%
s 88186
 
9.1%
a 86029
 
8.9%
o 65644
 
6.8%
n 64951
 
6.7%
i 64329
 
6.6%
t 60708
 
6.3%
l 59578
 
6.1%
r 58489
 
6.0%
c 24647
 
2.5%
Other values (39) 305986
31.5%
Common
ValueCountFrequency (%)
84768
93.3%
6 2453
 
2.7%
7 2451
 
2.7%
. 873
 
1.0%
- 189
 
0.2%
/ 84
 
0.1%
' 6
 
< 0.1%
3 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1060808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 91435
 
8.6%
s 88186
 
8.3%
a 86029
 
8.1%
84768
 
8.0%
o 65644
 
6.2%
n 64951
 
6.1%
i 64329
 
6.1%
t 60708
 
5.7%
l 59578
 
5.6%
r 58489
 
5.5%
Other values (47) 336691
31.7%

matchup_away
Text

Distinct2292
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:41.778818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.999056288
Min length8

Characters and Unicode

Total characters591220
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique176 ?
Unique (%)0.3%

Sample

1st rowNYK @ HUS
2nd rowPIT @ BOM
3rd rowBOS @ PRO
4th rowNYK @ CHS
5th rowWAS @ DEF
ValueCountFrequency (%)
65698
33.3%
bos 6207
 
3.1%
nyk 5877
 
3.0%
lal 5203
 
2.6%
det 5110
 
2.6%
chi 4608
 
2.3%
mil 4510
 
2.3%
phx 4490
 
2.3%
atl 4450
 
2.3%
hou 4333
 
2.2%
Other values (107) 86608
43.9%
2023-07-13T22:04:41.971025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
131396
22.2%
@ 65698
11.1%
A 39486
 
6.7%
L 39033
 
6.6%
S 31435
 
5.3%
N 29700
 
5.0%
O 26696
 
4.5%
H 26122
 
4.4%
C 22875
 
3.9%
I 22703
 
3.8%
Other values (17) 156076
26.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 394126
66.7%
Space Separator 131396
 
22.2%
Other Punctuation 65698
 
11.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 39486
 
10.0%
L 39033
 
9.9%
S 31435
 
8.0%
N 29700
 
7.5%
O 26696
 
6.8%
H 26122
 
6.6%
C 22875
 
5.8%
I 22703
 
5.8%
E 18789
 
4.8%
T 18493
 
4.7%
Other values (15) 118794
30.1%
Space Separator
ValueCountFrequency (%)
131396
100.0%
Other Punctuation
ValueCountFrequency (%)
@ 65698
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 394126
66.7%
Common 197094
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 39486
 
10.0%
L 39033
 
9.9%
S 31435
 
8.0%
N 29700
 
7.5%
O 26696
 
6.8%
H 26122
 
6.6%
C 22875
 
5.8%
I 22703
 
5.8%
E 18789
 
4.8%
T 18493
 
4.7%
Other values (15) 118794
30.1%
Common
ValueCountFrequency (%)
131396
66.7%
@ 65698
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 591220
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
131396
22.2%
@ 65698
11.1%
A 39486
 
6.7%
L 39033
 
6.6%
S 31435
 
5.3%
N 29700
 
5.0%
O 26696
 
4.5%
H 26122
 
4.4%
C 22875
 
3.9%
I 22703
 
3.8%
Other values (17) 156076
26.4%

wl_away
Text

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size513.4 KiB
2023-07-13T22:04:42.023697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters65696
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowL
3rd rowL
4th rowL
5th rowW
ValueCountFrequency (%)
l 40649
61.9%
w 25047
38.1%
2023-07-13T22:04:42.117089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 40649
61.9%
W 25047
38.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 65696
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 40649
61.9%
W 25047
38.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 65696
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 40649
61.9%
W 25047
38.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 40649
61.9%
W 25047
38.1%

fgm_away
Real number (ℝ)

Distinct64
Distinct (%)0.1%
Missing13
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean38.35184593
Minimum4
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:42.180251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile28
Q134
median38
Q343
95-th percentile49
Maximum82
Range78
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.480155126
Coefficient of variation (CV)0.1689659251
Kurtosis0.4429100326
Mean38.35184593
Median Absolute Deviation (MAD)4
Skewness0.02626604128
Sum2519141
Variance41.99241046
MonotonicityNot monotonic
2023-07-13T22:04:42.239008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 4279
 
6.5%
39 4216
 
6.4%
38 4146
 
6.3%
40 4008
 
6.1%
41 3876
 
5.9%
36 3860
 
5.9%
35 3602
 
5.5%
42 3361
 
5.1%
34 3226
 
4.9%
43 3178
 
4.8%
Other values (54) 27933
42.5%
ValueCountFrequency (%)
4 1
 
< 0.1%
6 1
 
< 0.1%
9 1
 
< 0.1%
13 2
 
< 0.1%
14 5
< 0.1%
ValueCountFrequency (%)
82 2
< 0.1%
78 2
< 0.1%
76 2
< 0.1%
74 1
< 0.1%
70 1
< 0.1%

fga_away
Real number (ℝ)

MISSING 

Distinct89
Distinct (%)0.2%
Missing15447
Missing (%)23.5%
Infinite0
Infinite (%)0.0%
Mean83.76878072
Minimum0
Maximum149
Zeros42
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:42.300905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile70
Q178
median83
Q389
95-th percentile99
Maximum149
Range149
Interquartile range (IQR)11

Descriptive statistics

Standard deviation9.124196101
Coefficient of variation (CV)0.1089211998
Kurtosis6.320811412
Mean83.76878072
Median Absolute Deviation (MAD)6
Skewness-0.3050860143
Sum4209465
Variance83.25095449
MonotonicityNot monotonic
2023-07-13T22:04:42.360719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82 2347
 
3.6%
83 2320
 
3.5%
84 2306
 
3.5%
85 2295
 
3.5%
81 2294
 
3.5%
80 2231
 
3.4%
86 2205
 
3.4%
79 2150
 
3.3%
87 2127
 
3.2%
78 1989
 
3.0%
Other values (79) 27987
42.6%
(Missing) 15447
23.5%
ValueCountFrequency (%)
0 42
0.1%
9 1
 
< 0.1%
31 1
 
< 0.1%
47 1
 
< 0.1%
50 1
 
< 0.1%
ValueCountFrequency (%)
149 2
< 0.1%
137 2
< 0.1%
136 1
< 0.1%
135 2
< 0.1%
133 2
< 0.1%

fg_pct_away
Real number (ℝ)

MISSING 

Distinct410
Distinct (%)0.8%
Missing15489
Missing (%)23.6%
Infinite0
Infinite (%)0.0%
Mean0.4549091
Minimum0.156
Maximum3.556
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:42.424447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.156
5-th percentile0.363
Q10.416
median0.455
Q30.494
95-th percentile0.551
Maximum3.556
Range3.4
Interquartile range (IQR)0.078

Descriptive statistics

Standard deviation0.05921879954
Coefficient of variation (CV)0.1301772146
Kurtosis149.605782
Mean0.4549091
Median Absolute Deviation (MAD)0.039
Skewness2.903756452
Sum22840.531
Variance0.003506866219
MonotonicityNot monotonic
2023-07-13T22:04:42.578331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 1469
 
2.2%
0.494 969
 
1.5%
0.506 857
 
1.3%
0.488 649
 
1.0%
0.429 547
 
0.8%
0.4 544
 
0.8%
0.432 536
 
0.8%
0.427 513
 
0.8%
0.438 509
 
0.8%
0.481 503
 
0.8%
Other values (400) 43113
65.6%
(Missing) 15489
 
23.6%
ValueCountFrequency (%)
0.156 1
< 0.1%
0.164 1
< 0.1%
0.181 1
< 0.1%
0.185 1
< 0.1%
0.187 1
< 0.1%
ValueCountFrequency (%)
3.556 1
< 0.1%
0.707 1
< 0.1%
0.687 1
< 0.1%
0.685 1
< 0.1%
0.684 1
< 0.1%

fg3m_away
Real number (ℝ)

MISSING  ZEROS 

Distinct32
Distinct (%)0.1%
Missing13218
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean5.638586128
Minimum0
Maximum35
Zeros5773
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:42.636376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile14
Maximum35
Range35
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.447714209
Coefficient of variation (CV)0.7887995515
Kurtosis0.5430331356
Mean5.638586128
Median Absolute Deviation (MAD)3
Skewness0.8562785465
Sum295913
Variance19.78216169
MonotonicityNot monotonic
2023-07-13T22:04:42.688368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 5773
8.8%
1 4815
 
7.3%
2 4665
 
7.1%
3 4613
 
7.0%
4 4536
 
6.9%
5 4408
 
6.7%
6 4174
 
6.4%
7 3716
 
5.7%
8 3052
 
4.6%
9 2750
 
4.2%
Other values (22) 9978
15.2%
(Missing) 13218
20.1%
ValueCountFrequency (%)
0 5773
8.8%
1 4815
7.3%
2 4665
7.1%
3 4613
7.0%
4 4536
6.9%
ValueCountFrequency (%)
35 2
< 0.1%
31 4
< 0.1%
29 3
< 0.1%
28 1
 
< 0.1%
27 3
< 0.1%

fg3a_away
Real number (ℝ)

MISSING 

Distinct68
Distinct (%)0.1%
Missing18683
Missing (%)28.4%
Infinite0
Infinite (%)0.0%
Mean17.77715623
Minimum0
Maximum90
Zeros279
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:42.749039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q110
median16
Q324
95-th percentile37
Maximum90
Range90
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.45815272
Coefficient of variation (CV)0.5882916587
Kurtosis0.1198313528
Mean17.77715623
Median Absolute Deviation (MAD)7
Skewness0.6565538732
Sum835793
Variance109.3729584
MonotonicityNot monotonic
2023-07-13T22:04:42.807348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 1924
 
2.9%
16 1918
 
2.9%
15 1881
 
2.9%
14 1858
 
2.8%
12 1848
 
2.8%
17 1798
 
2.7%
10 1734
 
2.6%
11 1730
 
2.6%
18 1693
 
2.6%
19 1600
 
2.4%
Other values (58) 29031
44.2%
(Missing) 18683
28.4%
ValueCountFrequency (%)
0 279
 
0.4%
1 615
0.9%
2 775
1.2%
3 1076
1.6%
4 1127
1.7%
ValueCountFrequency (%)
90 2
< 0.1%
80 2
< 0.1%
70 1
< 0.1%
69 1
< 0.1%
66 2
< 0.1%

fg3_pct_away
Real number (ℝ)

MISSING  ZEROS 

Distinct384
Distinct (%)0.8%
Missing18962
Missing (%)28.9%
Infinite0
Infinite (%)0.0%
Mean0.3366392075
Minimum0
Maximum1
Zeros2429
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:42.868080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.25
median0.333
Q30.424
95-th percentile0.56075
Maximum1
Range1
Interquartile range (IQR)0.174

Descriptive statistics

Standard deviation0.1472972789
Coefficient of variation (CV)0.4375523577
Kurtosis1.642287309
Mean0.3366392075
Median Absolute Deviation (MAD)0.083
Skewness0.1371050967
Sum15733.17
Variance0.02169648838
MonotonicityNot monotonic
2023-07-13T22:04:42.929048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.333 3374
 
5.1%
0.5 2472
 
3.8%
0 2429
 
3.7%
0.25 2024
 
3.1%
0.4 1713
 
2.6%
0.286 1287
 
2.0%
0.2 1192
 
1.8%
0.375 1111
 
1.7%
0.429 1073
 
1.6%
0.3 811
 
1.2%
Other values (374) 29250
44.5%
(Missing) 18962
28.9%
ValueCountFrequency (%)
0 2429
3.7%
0.04 1
 
< 0.1%
0.043 1
 
< 0.1%
0.05 4
 
< 0.1%
0.053 10
 
< 0.1%
ValueCountFrequency (%)
1 192
0.3%
0.875 1
 
< 0.1%
0.857 10
 
< 0.1%
0.833 21
 
< 0.1%
0.818 2
 
< 0.1%

ftm_away
Real number (ℝ)

Distinct56
Distinct (%)0.1%
Missing13
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean19.78584152
Minimum0
Maximum57
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:42.987438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q115
median19
Q324
95-th percentile32
Maximum57
Range57
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.842215507
Coefficient of variation (CV)0.3458137225
Kurtosis0.237670227
Mean19.78584152
Median Absolute Deviation (MAD)5
Skewness0.4511837349
Sum1299633
Variance46.81591305
MonotonicityNot monotonic
2023-07-13T22:04:43.045189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 3884
 
5.9%
17 3859
 
5.9%
19 3822
 
5.8%
16 3736
 
5.7%
20 3730
 
5.7%
21 3473
 
5.3%
15 3418
 
5.2%
22 3374
 
5.1%
14 3229
 
4.9%
23 3094
 
4.7%
Other values (46) 30066
45.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 9
 
< 0.1%
2 7
 
< 0.1%
3 50
0.1%
4 98
0.1%
ValueCountFrequency (%)
57 2
 
< 0.1%
56 1
 
< 0.1%
55 1
 
< 0.1%
52 1
 
< 0.1%
51 9
< 0.1%

fta_away
Real number (ℝ)

MISSING 

Distinct70
Distinct (%)0.1%
Missing3004
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean26.03606406
Minimum0
Maximum91
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:43.106332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q120
median25
Q331
95-th percentile41
Maximum91
Range91
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.496634861
Coefficient of variation (CV)0.3263409877
Kurtosis0.2912558636
Mean26.03606406
Median Absolute Deviation (MAD)6
Skewness0.4480831102
Sum1632305
Variance72.19280397
MonotonicityNot monotonic
2023-07-13T22:04:43.164920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 3023
 
4.6%
25 2986
 
4.5%
23 2924
 
4.5%
21 2879
 
4.4%
26 2871
 
4.4%
22 2871
 
4.4%
27 2784
 
4.2%
28 2749
 
4.2%
20 2656
 
4.0%
29 2466
 
3.8%
Other values (60) 34485
52.5%
(Missing) 3004
 
4.6%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 1
 
< 0.1%
2 4
 
< 0.1%
3 4
 
< 0.1%
4 21
< 0.1%
ValueCountFrequency (%)
91 1
< 0.1%
74 2
< 0.1%
67 1
< 0.1%
66 1
< 0.1%
65 1
< 0.1%

ft_pct_away
Real number (ℝ)

MISSING 

Distinct439
Distinct (%)0.7%
Missing3006
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean0.7526657787
Minimum0.143
Maximum5.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:43.222840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.143
5-th percentile0.583
Q10.69
median0.758
Q30.821
95-th percentile0.909
Maximum5.25
Range5.107
Interquartile range (IQR)0.131

Descriptive statistics

Standard deviation0.1034930169
Coefficient of variation (CV)0.1375019562
Kurtosis106.9703707
Mean0.7526657787
Median Absolute Deviation (MAD)0.066
Skewness2.389339572
Sum47186.123
Variance0.01071080455
MonotonicityNot monotonic
2023-07-13T22:04:43.283702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.75 2570
 
3.9%
0.667 2296
 
3.5%
0.8 1973
 
3.0%
0.833 1382
 
2.1%
0.714 1349
 
2.1%
0.778 1133
 
1.7%
0.857 1055
 
1.6%
0.727 869
 
1.3%
0.7 841
 
1.3%
0.818 819
 
1.2%
Other values (429) 48405
73.7%
(Missing) 3006
 
4.6%
ValueCountFrequency (%)
0.143 1
< 0.1%
0.176 1
< 0.1%
0.2 2
< 0.1%
0.22 1
< 0.1%
0.231 1
< 0.1%
ValueCountFrequency (%)
5.25 1
< 0.1%
5 1
< 0.1%
3 1
< 0.1%
2.4 1
< 0.1%
1.833 1
< 0.1%

oreb_away
Real number (ℝ)

MISSING 

Distinct37
Distinct (%)0.1%
Missing18936
Missing (%)28.8%
Infinite0
Infinite (%)0.0%
Mean11.68686113
Minimum0
Maximum40
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:43.340231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q19
median11
Q314
95-th percentile19
Maximum40
Range40
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.281311413
Coefficient of variation (CV)0.36633544
Kurtosis0.3838004784
Mean11.68686113
Median Absolute Deviation (MAD)3
Skewness0.5180005257
Sum546501
Variance18.32962741
MonotonicityNot monotonic
2023-07-13T22:04:43.397598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
10 4495
 
6.8%
11 4478
 
6.8%
12 4212
 
6.4%
9 4038
 
6.1%
13 3759
 
5.7%
8 3637
 
5.5%
14 3358
 
5.1%
7 2885
 
4.4%
15 2803
 
4.3%
16 2240
 
3.4%
Other values (27) 10857
16.5%
(Missing) 18936
28.8%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 21
 
< 0.1%
2 117
 
0.2%
3 369
0.6%
4 756
1.2%
ValueCountFrequency (%)
40 1
< 0.1%
38 1
< 0.1%
37 1
< 0.1%
33 2
< 0.1%
32 1
< 0.1%

dreb_away
Real number (ℝ)

MISSING 

Distinct50
Distinct (%)0.1%
Missing18998
Missing (%)28.9%
Infinite0
Infinite (%)0.0%
Mean30.23807281
Minimum0
Maximum60
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:43.460648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21
Q126
median30
Q334
95-th percentile40
Maximum60
Range60
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.588674575
Coefficient of variation (CV)0.1848224459
Kurtosis0.09777658112
Mean30.23807281
Median Absolute Deviation (MAD)4
Skewness0.2162920787
Sum1412118
Variance31.23328351
MonotonicityNot monotonic
2023-07-13T22:04:43.519181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 3344
 
5.1%
29 3335
 
5.1%
31 3170
 
4.8%
32 3112
 
4.7%
27 3090
 
4.7%
28 3084
 
4.7%
33 2773
 
4.2%
26 2678
 
4.1%
34 2461
 
3.7%
25 2360
 
3.6%
Other values (40) 17293
26.3%
(Missing) 18998
28.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
4 1
 
< 0.1%
8 1
 
< 0.1%
10 2
 
< 0.1%
12 7
< 0.1%
ValueCountFrequency (%)
60 1
< 0.1%
56 2
< 0.1%
55 2
< 0.1%
54 1
< 0.1%
53 1
< 0.1%

reb_away
Real number (ℝ)

MISSING 

Distinct68
Distinct (%)0.1%
Missing15725
Missing (%)23.9%
Infinite0
Infinite (%)0.0%
Mean42.11964461
Minimum0
Maximum90
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:43.581899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31
Q137
median42
Q347
95-th percentile54
Maximum90
Range90
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.867395658
Coefficient of variation (CV)0.1630449573
Kurtosis0.72510123
Mean42.11964461
Median Absolute Deviation (MAD)5
Skewness0.269954548
Sum2104845
Variance47.16112313
MonotonicityNot monotonic
2023-07-13T22:04:43.639736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41 3049
 
4.6%
42 2974
 
4.5%
40 2872
 
4.4%
43 2845
 
4.3%
39 2800
 
4.3%
38 2714
 
4.1%
44 2702
 
4.1%
45 2523
 
3.8%
37 2476
 
3.8%
46 2305
 
3.5%
Other values (58) 22713
34.6%
(Missing) 15725
23.9%
ValueCountFrequency (%)
0 11
< 0.1%
13 1
 
< 0.1%
15 1
 
< 0.1%
17 1
 
< 0.1%
18 1
 
< 0.1%
ValueCountFrequency (%)
90 1
 
< 0.1%
82 1
 
< 0.1%
81 1
 
< 0.1%
79 4
< 0.1%
78 1
 
< 0.1%

ast_away
Real number (ℝ)

MISSING 

Distinct50
Distinct (%)0.1%
Missing15801
Missing (%)24.1%
Infinite0
Infinite (%)0.0%
Mean22.13541896
Minimum0
Maximum89
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:43.699076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q118
median22
Q326
95-th percentile31
Maximum89
Range89
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.380804944
Coefficient of variation (CV)0.2430857511
Kurtosis0.6936986099
Mean22.13541896
Median Absolute Deviation (MAD)4
Skewness0.3141707767
Sum1104491
Variance28.95306185
MonotonicityNot monotonic
2023-07-13T22:04:43.757566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 3709
 
5.6%
20 3689
 
5.6%
21 3581
 
5.5%
24 3507
 
5.3%
23 3470
 
5.3%
19 3468
 
5.3%
25 2993
 
4.6%
18 2941
 
4.5%
26 2711
 
4.1%
17 2571
 
3.9%
Other values (40) 17257
26.3%
(Missing) 15801
24.1%
ValueCountFrequency (%)
0 11
< 0.1%
3 1
 
< 0.1%
4 2
 
< 0.1%
5 3
 
< 0.1%
6 8
< 0.1%
ValueCountFrequency (%)
89 1
 
< 0.1%
52 1
 
< 0.1%
51 2
< 0.1%
49 1
 
< 0.1%
47 3
< 0.1%

stl_away
Real number (ℝ)

MISSING 

Distinct26
Distinct (%)0.1%
Missing18849
Missing (%)28.7%
Infinite0
Infinite (%)0.0%
Mean7.854148434
Minimum0
Maximum27
Zeros41
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:43.809985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q16
median8
Q310
95-th percentile13
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.031766287
Coefficient of variation (CV)0.3860082748
Kurtosis0.3216565764
Mean7.854148434
Median Absolute Deviation (MAD)2
Skewness0.4624151634
Sum367959
Variance9.191606819
MonotonicityNot monotonic
2023-07-13T22:04:43.862135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
7 6246
 
9.5%
8 6104
 
9.3%
6 5830
 
8.9%
9 5135
 
7.8%
5 4629
 
7.0%
10 4240
 
6.5%
4 3231
 
4.9%
11 3196
 
4.9%
12 2120
 
3.2%
3 1738
 
2.6%
Other values (16) 4380
 
6.7%
(Missing) 18849
28.7%
ValueCountFrequency (%)
0 41
 
0.1%
1 214
 
0.3%
2 740
 
1.1%
3 1738
2.6%
4 3231
4.9%
ValueCountFrequency (%)
27 1
 
< 0.1%
24 3
< 0.1%
23 4
< 0.1%
22 1
 
< 0.1%
21 7
< 0.1%

blk_away
Real number (ℝ)

MISSING  ZEROS 

Distinct20
Distinct (%)< 0.1%
Missing18625
Missing (%)28.3%
Infinite0
Infinite (%)0.0%
Mean4.681537187
Minimum0
Maximum19
Zeros862
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:43.911145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile9
Maximum19
Range19
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.500819563
Coefficient of variation (CV)0.5341876959
Kurtosis0.5929922144
Mean4.681537187
Median Absolute Deviation (MAD)2
Skewness0.6673296631
Sum220374
Variance6.254098487
MonotonicityNot monotonic
2023-07-13T22:04:43.957496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4 7812
11.9%
3 7264
 
11.1%
5 6946
 
10.6%
6 5675
 
8.6%
2 5531
 
8.4%
7 3984
 
6.1%
1 2847
 
4.3%
8 2588
 
3.9%
9 1606
 
2.4%
10 921
 
1.4%
Other values (10) 1899
 
2.9%
(Missing) 18625
28.3%
ValueCountFrequency (%)
0 862
 
1.3%
1 2847
 
4.3%
2 5531
8.4%
3 7264
11.1%
4 7812
11.9%
ValueCountFrequency (%)
19 4
 
< 0.1%
18 2
 
< 0.1%
17 2
 
< 0.1%
16 16
< 0.1%
15 29
< 0.1%

tov_away
Real number (ℝ)

MISSING 

Distinct39
Distinct (%)0.1%
Missing18685
Missing (%)28.4%
Infinite0
Infinite (%)0.0%
Mean15.19985961
Minimum0
Maximum40
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:44.013558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q112
median15
Q318
95-th percentile23
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.299797768
Coefficient of variation (CV)0.2828840448
Kurtosis0.3556188208
Mean15.19985961
Median Absolute Deviation (MAD)3
Skewness0.403369454
Sum714591
Variance18.48826085
MonotonicityNot monotonic
2023-07-13T22:04:44.069716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
14 4478
 
6.8%
15 4387
 
6.7%
13 4231
 
6.4%
16 4214
 
6.4%
17 3726
 
5.7%
12 3705
 
5.6%
18 3254
 
5.0%
11 3028
 
4.6%
19 2608
 
4.0%
10 2366
 
3.6%
Other values (29) 11016
16.8%
(Missing) 18685
28.4%
ValueCountFrequency (%)
0 7
 
< 0.1%
2 1
 
< 0.1%
3 18
 
< 0.1%
4 44
 
0.1%
5 110
0.2%
ValueCountFrequency (%)
40 1
 
< 0.1%
38 1
 
< 0.1%
37 1
 
< 0.1%
36 3
< 0.1%
35 1
 
< 0.1%

pf_away
Real number (ℝ)

MISSING 

Distinct61
Distinct (%)0.1%
Missing2851
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean23.09728388
Minimum0
Maximum115
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:44.130314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q120
median23
Q326
95-th percentile32
Maximum115
Range115
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.227207601
Coefficient of variation (CV)0.2263126534
Kurtosis5.305823956
Mean23.09728388
Median Absolute Deviation (MAD)3
Skewness0.6529980815
Sum1451595
Variance27.3236993
MonotonicityNot monotonic
2023-07-13T22:04:44.192923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 4970
 
7.6%
21 4949
 
7.5%
23 4847
 
7.4%
24 4640
 
7.1%
20 4433
 
6.7%
25 4352
 
6.6%
19 3919
 
6.0%
26 3820
 
5.8%
27 3323
 
5.1%
18 3195
 
4.9%
Other values (51) 20399
31.0%
ValueCountFrequency (%)
0 4
< 0.1%
2 3
< 0.1%
3 4
< 0.1%
5 3
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
115 1
< 0.1%
114 1
< 0.1%
104 1
< 0.1%
90 1
< 0.1%
87 1
< 0.1%

pts_away
Real number (ℝ)

Distinct133
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.9915675
Minimum19
Maximum196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:44.255625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile77
Q192
median101
Q3110
95-th percentile124
Maximum196
Range177
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.41875524
Coefficient of variation (CV)0.142771873
Kurtosis0.4667169582
Mean100.9915675
Median Absolute Deviation (MAD)9
Skewness-0.01008482941
Sum6634944
Variance207.9005027
MonotonicityNot monotonic
2023-07-13T22:04:44.315892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102 1902
 
2.9%
99 1897
 
2.9%
100 1890
 
2.9%
103 1889
 
2.9%
98 1853
 
2.8%
101 1853
 
2.8%
105 1822
 
2.8%
106 1811
 
2.8%
104 1806
 
2.7%
96 1801
 
2.7%
Other values (123) 47174
71.8%
ValueCountFrequency (%)
19 1
< 0.1%
33 1
< 0.1%
38 1
< 0.1%
40 2
< 0.1%
43 2
< 0.1%
ValueCountFrequency (%)
196 2
< 0.1%
186 1
< 0.1%
184 2
< 0.1%
182 2
< 0.1%
178 2
< 0.1%

plus_minus_away
Real number (ℝ)

Distinct121
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.627568571
Minimum-73
Maximum68
Zeros0
Zeros (%)0.0%
Negative40652
Negative (%)61.9%
Memory size513.4 KiB
2023-07-13T22:04:44.378275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-73
5-th percentile-25
Q1-12
median-4
Q35
95-th percentile18
Maximum68
Range141
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.09139486
Coefficient of variation (CV)-3.608862137
Kurtosis0.3444088881
Mean-3.627568571
Median Absolute Deviation (MAD)9
Skewness0.01722224077
Sum-238324
Variance171.3846195
MonotonicityNot monotonic
2023-07-13T22:04:44.436814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-7 2409
 
3.7%
-5 2396
 
3.6%
-6 2252
 
3.4%
-8 2248
 
3.4%
-4 2212
 
3.4%
-2 2202
 
3.4%
-9 2170
 
3.3%
-3 2164
 
3.3%
-10 1977
 
3.0%
2 1915
 
2.9%
Other values (111) 43753
66.6%
ValueCountFrequency (%)
-73 1
< 0.1%
-68 1
< 0.1%
-65 1
< 0.1%
-63 1
< 0.1%
-62 2
< 0.1%
ValueCountFrequency (%)
68 1
 
< 0.1%
58 1
 
< 0.1%
57 1
 
< 0.1%
56 4
< 0.1%
54 1
 
< 0.1%

video_available_away
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2013303297
Minimum0
Maximum1
Zeros52471
Zeros (%)79.9%
Negative0
Negative (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:44.600130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4009973511
Coefficient of variation (CV)1.991738412
Kurtosis0.2191516246
Mean0.2013303297
Median Absolute Deviation (MAD)0
Skewness1.48967948
Sum13227
Variance0.1607988756
MonotonicityNot monotonic
2023-07-13T22:04:44.642135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 52471
79.9%
1 13227
 
20.1%
ValueCountFrequency (%)
0 52471
79.9%
1 13227
 
20.1%
ValueCountFrequency (%)
1 13227
 
20.1%
0 52471
79.9%

season_type
Text

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size513.4 KiB
2023-07-13T22:04:44.730210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length14
Median length14
Mean length13.54391306
Min length8

Characters and Unicode

Total characters889808
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRegular Season
2nd rowRegular Season
3rd rowRegular Season
4th rowRegular Season
5th rowRegular Season
ValueCountFrequency (%)
season 61728
48.4%
regular 60192
47.2%
playoffs 3842
 
3.0%
pre 1536
 
1.2%
all 65
 
0.1%
star 65
 
0.1%
all-star 63
 
< 0.1%
2023-07-13T22:04:44.881926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 125890
14.1%
e 123456
13.9%
s 65570
7.4%
o 65570
7.4%
l 64290
7.2%
r 61856
7.0%
S 61856
7.0%
61793
6.9%
n 61728
6.9%
R 60192
6.8%
Other values (8) 137607
15.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 700398
78.7%
Uppercase Letter 127554
 
14.3%
Space Separator 61793
 
6.9%
Dash Punctuation 63
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 125890
18.0%
e 123456
17.6%
s 65570
9.4%
o 65570
9.4%
l 64290
9.2%
r 61856
8.8%
n 61728
8.8%
u 60192
8.6%
g 60192
8.6%
f 7684
 
1.1%
Other values (2) 3970
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
S 61856
48.5%
R 60192
47.2%
P 5378
 
4.2%
A 128
 
0.1%
Space Separator
ValueCountFrequency (%)
61793
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 827952
93.0%
Common 61856
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 125890
15.2%
e 123456
14.9%
s 65570
7.9%
o 65570
7.9%
l 64290
7.8%
r 61856
7.5%
S 61856
7.5%
n 61728
7.5%
R 60192
7.3%
u 60192
7.3%
Other values (6) 77352
9.3%
Common
ValueCountFrequency (%)
61793
99.9%
- 63
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 889808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 125890
14.1%
e 123456
13.9%
s 65570
7.4%
o 65570
7.4%
l 64290
7.2%
r 61856
7.0%
S 61856
7.0%
61793
6.9%
n 61728
6.9%
R 60192
6.8%
Other values (8) 137607
15.5%